Evolution of the semiconductor manufacturing industry is placing ever greater demands on yield management and, in particular, on metrology and inspection systems. Critical dimensions continue to shrink. Economics is driving the industry to decrease the time for achieving high-yield, high-value production. Minimizing the total time from detecting a yield problem to fixing it determines the return-on-investment for a semiconductor manufacturer.
Fabricating semiconductor devices, such as logic and memory devices, typically includes processing a semiconductor wafer using a large number of fabrication processes to form various features and multiple levels of the semiconductor devices. For example, lithography is a semiconductor fabrication process that involves transferring a pattern from a reticle to a photoresist arranged on a semiconductor wafer. Additional examples of semiconductor fabrication processes include, but are not limited to, chemical-mechanical polishing (CMP), etch, deposition, and ion implantation. Multiple semiconductor devices may be fabricated in an arrangement on a single semiconductor wafer and then separated into individual semiconductor devices.
Inspection processes are used at various steps during semiconductor manufacturing to detect defects on wafers to promote higher yield in the manufacturing process and, thus, higher profits. Inspection has always been an important part of fabricating semiconductor devices such as integrated circuits (ICs). However, as the dimensions of semiconductor devices decrease, inspection becomes even more important to the successful manufacture of acceptable semiconductor devices because smaller defects can cause the devices to fail. For instance, as the dimensions of semiconductor devices decrease, detection of defects of decreasing size has become necessary since even relatively small defects may cause unwanted aberrations in the semiconductor devices.
Defect review typically involves high resolution imaging and classification of defects that were flagged by an inspection process using either a high magnification optical system or a scanning electron microscope (SEM). Defect review is typically performed at discrete locations on specimens where defects have been detected by inspection. The higher resolution data for the defects generated by defect review is more suitable for determining attributes of the defects such as profile, roughness, or more accurate size information.
Defect review is a process by which a review tool reviews defects acquired by an inspector or inspection tool. Defect review also results in classification of defects and differentiation, or separation of defect types based on a set of calculated defect attributes. Advances in machine learning (or deep learning) methodologies have made it an attractive framework for use in defect detection and classification. While such frameworks have proven useful for defect classification and other functions, the frameworks themselves also make it difficult to know whether the machine learning framework is operating correctly. For example, in the case of defect classification, currently used approaches for performing quality assurance on a classifier include classical metrics in machine learning such as accuracy, confusion matrix, and sensitivity on an offline test dataset and an online and/or in-field evaluation. In addition, currently used approaches for performing data augmentation include having a domain expert or an algorithm expert guide the process. There are, however, a number of disadvantages for currently-used quality assurance and data augmentation methods and systems. For example, the currently-used quality assurance approaches described above cannot identify a situation in which a classifier makes a correct prediction based on wrong causal features, especially with deep learning classifiers. In another example, the currently-used quality assurance approaches described above treat machine learning algorithms as a black box. In an additional example, the currently-used approaches for performing data augmentation cannot be used to directly improve or correct a poorly-trained classifier.
Accordingly, improved defect detection and classification is needed.